Assessment of the synoptic variability of the Antarctic marginal ice zone with in Situ observations

Master Thesis


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Knowledge of sea ice variability, which contributes to the detection of climate change trends, stems primarily from remote sensing information. However, sea ice in the Southern Ocean is characterised by large variability that remains unresolved and limits our confidence on the remotely sensed products. Although one of the biggest seasonal changes on Earth is the annual advance and retreat of the Antarctic sea ice cover, relatively little attention has been given to the processes by which the marginal ice zone (MIZ) edge forms and responds to synoptic events. This study aimed to assess the seasonal sea ice extent (SIE) of the MIZ by comparing sea ice observations estimated from aboard ship to high resolution passive microwave (PM) satellite imagery when transecting the MIZ. To achieve this, sea ice concentration (SIC) was derived from two AMSR (Advanced Microwave Scanning Radiometer ) products; the ARTIST (Arctic Radiation and Turbulence Interaction STudy) Sea Ice (ASI-AMSR ) and the bootstrap (BST-AMSR ). Theice concentration estimated from these PM satellite products was assessed against SIC observations collected from the S.A. Agulhas II (using the Antarctic Sea Ice Processes and Climate (ASPeCt) protocol). This assessment took place over summer and winter for the years 2016 and 2017. After evaluating how well these PM-SIC estimates compared against the ASPeCt SIC observations, we found that there was good correlation over summer MIZ conditions, while over winter MIZ conditions the correlation was relatively poor. This highlighted winter limitations inherent in PM SIC estimates. Therefore, from these comparison results, an analysis of the seasonal SIE was accomplished while being aware of the winter limitations linked to the PM products. We inferred that the MIZ acts as an indicator for what the evolution of winter SIE might look like over the following months. In addition to winter limitations associated with PM-SIC retrievals, the ASPeCt SIC estimates, based on human interpretation of the sea ice conditions, was limited because of subjective bias. This resulted in the development of an algorithm to automatically acquire SIC from image stills and videos. This method can be used to obtain quantitative seaice data from vessels of opportunity without the need to have trained personnel on-board. In summary, this study assesses seasonal MIZ SIE within the Atlantic sector after highlighting the limitations associated with various SIC-retrieval methods.